Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations2260701
Missing cells318111
Missing cells (%)0.7%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory345.0 MiB
Average record size in memory160.0 B

Variable types

Numeric11
Categorical8
Text1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
fico_range_high is highly overall correlated with fico_range_lowHigh correlation
fico_range_low is highly overall correlated with fico_range_highHigh correlation
grade is highly overall correlated with int_rate and 1 other fieldsHigh correlation
installment is highly overall correlated with loan_amntHigh correlation
int_rate is highly overall correlated with grade and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with installmentHigh correlation
open_acc is highly overall correlated with total_accHigh correlation
sub_grade is highly overall correlated with grade and 1 other fieldsHigh correlation
total_acc is highly overall correlated with open_accHigh correlation
loan_status is highly imbalanced (51.8%) Imbalance
emp_title has 167002 (7.4%) missing values Missing
emp_length has 146940 (6.5%) missing values Missing
annual_inc is highly skewed (γ1 = 493.8860884) Skewed
dti is highly skewed (γ1 = 29.20185447) Skewed

Reproduction

Analysis started2025-07-17 01:53:27.635234
Analysis finished2025-07-17 01:55:17.716755
Duration1 minute and 50.08 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

loan_amnt
Real number (ℝ)

High correlation 

Distinct1572
Distinct (%)0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15046.931
Minimum500
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:17.753118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3250
Q18000
median12900
Q320000
95-th percentile35000
Maximum40000
Range39500
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9190.2455
Coefficient of variation (CV)0.61077208
Kurtosis-0.11943916
Mean15046.931
Median Absolute Deviation (MAD)6200
Skewness0.77778233
Sum3.4016116 × 1010
Variance84460612
MonotonicityNot monotonic
2025-07-17T10:55:17.801359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 187236
 
8.3%
20000 131006
 
5.8%
15000 123226
 
5.5%
12000 121681
 
5.4%
35000 86285
 
3.8%
5000 84765
 
3.7%
8000 75033
 
3.3%
6000 72089
 
3.2%
25000 66453
 
2.9%
16000 66418
 
2.9%
Other values (1562) 1246476
55.1%
ValueCountFrequency (%)
500 11
< 0.1%
550 1
 
< 0.1%
600 6
< 0.1%
700 3
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 3
 
< 0.1%
850 1
 
< 0.1%
900 4
 
< 0.1%
925 1
 
< 0.1%
ValueCountFrequency (%)
40000 33368
1.5%
39975 11
 
< 0.1%
39950 10
 
< 0.1%
39925 14
 
< 0.1%
39900 24
 
< 0.1%
39875 8
 
< 0.1%
39850 7
 
< 0.1%
39825 13
 
< 0.1%
39800 10
 
< 0.1%
39775 13
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size17.2 MiB
36 months
1609754 
60 months
650914 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters22606680
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 60 months
4th row 60 months
5th row 60 months

Common Values

ValueCountFrequency (%)
36 months 1609754
71.2%
60 months 650914
28.8%
(Missing) 33
 
< 0.1%

Length

2025-07-17T10:55:17.842676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T10:55:17.871529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
months 2260668
50.0%
36 1609754
35.6%
60 650914
 
14.4%

Most occurring characters

ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22606680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22606680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22606680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

int_rate
Real number (ℝ)

High correlation 

Distinct673
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.092829
Minimum5.31
Maximum30.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:17.906696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.31
5-th percentile6.49
Q19.49
median12.62
Q315.99
95-th percentile22.15
Maximum30.99
Range25.68
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.8321384
Coefficient of variation (CV)0.36906755
Kurtosis0.59402457
Mean13.092829
Median Absolute Deviation (MAD)3.18
Skewness0.76807056
Sum29598540
Variance23.349561
MonotonicityNot monotonic
2025-07-17T10:55:17.956432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.99 53869
 
2.4%
5.32 47171
 
2.1%
10.99 44165
 
2.0%
13.99 43025
 
1.9%
11.49 32010
 
1.4%
16.99 30564
 
1.4%
12.99 29276
 
1.3%
7.89 28514
 
1.3%
9.17 27835
 
1.2%
15.61 25208
 
1.1%
Other values (663) 1899031
84.0%
ValueCountFrequency (%)
5.31 8613
 
0.4%
5.32 47171
2.1%
5.42 573
 
< 0.1%
5.79 410
 
< 0.1%
5.93 1812
 
0.1%
5.99 347
 
< 0.1%
6 697
 
< 0.1%
6.03 10755
 
0.5%
6.07 5020
 
0.2%
6.08 5639
 
0.2%
ValueCountFrequency (%)
30.99 819
< 0.1%
30.94 733
< 0.1%
30.89 699
< 0.1%
30.84 755
< 0.1%
30.79 1572
0.1%
30.75 1075
< 0.1%
30.74 456
 
< 0.1%
30.65 983
< 0.1%
30.49 512
 
< 0.1%
30.17 1236
0.1%

installment
Real number (ℝ)

High correlation 

Distinct93301
Distinct (%)4.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean445.80682
Minimum4.93
Maximum1719.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:18.002570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.93
5-th percentile110.43
Q1251.65
median377.99
Q3593.32
95-th percentile984.47
Maximum1719.83
Range1714.9
Interquartile range (IQR)341.67

Descriptive statistics

Standard deviation267.17353
Coefficient of variation (CV)0.59930338
Kurtosis0.68987904
Mean445.80682
Median Absolute Deviation (MAD)157.63
Skewness1.0017806
Sum1.0078212 × 109
Variance71381.698
MonotonicityNot monotonic
2025-07-17T10:55:18.051080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301.15 4420
 
0.2%
332.1 4153
 
0.2%
361.38 3704
 
0.2%
327.34 3353
 
0.1%
602.3 3095
 
0.1%
451.73 3076
 
0.1%
329.72 2614
 
0.1%
166.05 2508
 
0.1%
498.15 2410
 
0.1%
180.69 2364
 
0.1%
Other values (93291) 2228971
98.6%
ValueCountFrequency (%)
4.93 1
< 0.1%
7.61 1
< 0.1%
14.01 1
< 0.1%
14.77 1
< 0.1%
15.67 1
< 0.1%
15.69 1
< 0.1%
15.75 1
< 0.1%
15.76 1
< 0.1%
15.91 1
< 0.1%
16.08 1
< 0.1%
ValueCountFrequency (%)
1719.83 2
 
< 0.1%
1717.63 1
 
< 0.1%
1715.42 2
 
< 0.1%
1714.54 6
< 0.1%
1691.28 2
 
< 0.1%
1670.15 1
 
< 0.1%
1647.03 1
 
< 0.1%
1628.08 2
 
< 0.1%
1618.24 3
< 0.1%
1618.03 7
< 0.1%

grade
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size17.2 MiB
B
663557 
C
650053 
A
433027 
D
324424 
E
135639 
Other values (2)
 
53968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2260668
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowC
5th rowF

Common Values

ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%
(Missing) 33
 
< 0.1%

Length

2025-07-17T10:55:18.093290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T10:55:18.121688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
b 663557
29.4%
c 650053
28.8%
a 433027
19.2%
d 324424
14.4%
e 135639
 
6.0%
f 41800
 
1.8%
g 12168
 
0.5%

Most occurring characters

ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%

sub_grade
Categorical

High correlation 

Distinct35
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size17.2 MiB
C1
 
145903
B5
 
140288
B4
 
139793
B3
 
131514
C2
 
131116
Other values (30)
1572054 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4521336
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC4
2nd rowC1
3rd rowB4
4th rowC5
5th rowF1

Common Values

ValueCountFrequency (%)
C1 145903
 
6.5%
B5 140288
 
6.2%
B4 139793
 
6.2%
B3 131514
 
5.8%
C2 131116
 
5.8%
C3 129193
 
5.7%
C4 127115
 
5.6%
B2 126621
 
5.6%
B1 125341
 
5.5%
C5 116726
 
5.2%
Other values (25) 947058
41.9%

Length

2025-07-17T10:55:18.160499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c1 145903
 
6.5%
b5 140288
 
6.2%
b4 139793
 
6.2%
b3 131514
 
5.8%
c2 131116
 
5.8%
c3 129193
 
5.7%
c4 127115
 
5.6%
b2 126621
 
5.6%
b1 125341
 
5.5%
c5 116726
 
5.2%
Other values (25) 947058
41.9%

Most occurring characters

ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

emp_title
Text

Missing 

Distinct512694
Distinct (%)24.5%
Missing167002
Missing (%)7.4%
Memory size17.2 MiB
2025-07-17T10:55:18.324107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length78
Median length57
Mean length15.596128
Min length1

Characters and Unicode

Total characters32653597
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387287 ?
Unique (%)18.5%

Sample

1st rowleadman
2nd rowEngineer
3rd rowtruck driver
4th rowInformation Systems Officer
5th rowContract Specialist
ValueCountFrequency (%)
manager 305625
 
7.2%
director 80439
 
1.9%
assistant 72742
 
1.7%
sales 72204
 
1.7%
supervisor 58337
 
1.4%
teacher 57228
 
1.3%
specialist 55010
 
1.3%
of 54332
 
1.3%
senior 52896
 
1.2%
engineer 52508
 
1.2%
Other values (101705) 3406104
79.8%
2025-07-17T10:55:18.562777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32653597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32653597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32653597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

emp_length
Categorical

Missing 

Distinct11
Distinct (%)< 0.1%
Missing146940
Missing (%)6.5%
Memory size17.2 MiB
10+ years
748005 
2 years
203677 
< 1 year
189988 
3 years
180753 
1 year
148403 
Other values (6)
642935 

Length

Max length9
Median length8
Mean length7.7274214
Min length6

Characters and Unicode

Total characters16333922
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row10+ years
3rd row10+ years
4th row10+ years
5th row3 years

Common Values

ValueCountFrequency (%)
10+ years 748005
33.1%
2 years 203677
 
9.0%
< 1 year 189988
 
8.4%
3 years 180753
 
8.0%
1 year 148403
 
6.6%
5 years 139698
 
6.2%
4 years 136605
 
6.0%
6 years 102628
 
4.5%
7 years 92695
 
4.1%
8 years 91914
 
4.1%
(Missing) 146940
 
6.5%

Length

2025-07-17T10:55:18.605331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 1775370
40.2%
10 748005
16.9%
1 338391
 
7.7%
year 338391
 
7.7%
2 203677
 
4.6%
189988
 
4.3%
3 180753
 
4.1%
5 139698
 
3.2%
4 136605
 
3.1%
6 102628
 
2.3%
Other values (3) 264004
 
6.0%

Most occurring characters

ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16333922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16333922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16333922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%

home_ownership
Categorical

Distinct6
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size17.2 MiB
MORTGAGE
1111450 
RENT
894929 
OWN
253057 
ANY
 
996
OTHER
 
182

Length

Max length8
Median length5
Mean length5.8542878
Min length3

Characters and Unicode

Total characters13234601
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowMORTGAGE
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
MORTGAGE 1111450
49.2%
RENT 894929
39.6%
OWN 253057
 
11.2%
ANY 996
 
< 0.1%
OTHER 182
 
< 0.1%
NONE 54
 
< 0.1%
(Missing) 33
 
< 0.1%

Length

2025-07-17T10:55:18.642398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T10:55:18.672422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 1111450
49.2%
rent 894929
39.6%
own 253057
 
11.2%
any 996
 
< 0.1%
other 182
 
< 0.1%
none 54
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13234601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13234601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13234601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

annual_inc
Real number (ℝ)

Skewed 

Distinct89368
Distinct (%)4.0%
Missing37
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean77992.429
Minimum0
Maximum1.1 × 108
Zeros1667
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:18.714527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27600
Q146000
median65000
Q393000
95-th percentile160000
Maximum1.1 × 108
Range1.1 × 108
Interquartile range (IQR)47000

Descriptive statistics

Standard deviation112696.2
Coefficient of variation (CV)1.4449633
Kurtosis439001.66
Mean77992.429
Median Absolute Deviation (MAD)22000
Skewness493.88609
Sum1.7631468 × 1011
Variance1.2700433 × 1010
MonotonicityNot monotonic
2025-07-17T10:55:18.762578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 87189
 
3.9%
50000 76355
 
3.4%
65000 64903
 
2.9%
70000 62078
 
2.7%
80000 59833
 
2.6%
40000 59684
 
2.6%
75000 58459
 
2.6%
45000 54534
 
2.4%
55000 51583
 
2.3%
100000 46977
 
2.1%
Other values (89358) 1639069
72.5%
ValueCountFrequency (%)
0 1667
0.1%
0.36 1
 
< 0.1%
1 42
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 5
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
110000000 1
< 0.1%
61000000 1
< 0.1%
10999200 1
< 0.1%
9930475 1
< 0.1%
9757200 1
< 0.1%
9573072 1
< 0.1%
9550000 1
< 0.1%
9522972 1
< 0.1%
9500000 1
< 0.1%
9300086 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size17.2 MiB
Source Verified
886231 
Not Verified
744806 
Verified
629631 

Length

Max length15
Median length12
Mean length12.062003
Min length8

Characters and Unicode

Total characters27268185
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowNot Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Source Verified 886231
39.2%
Not Verified 744806
32.9%
Verified 629631
27.9%
(Missing) 33
 
< 0.1%

Length

2025-07-17T10:55:18.803653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T10:55:18.829504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
verified 2260668
58.1%
source 886231
 
22.8%
not 744806
 
19.1%

Most occurring characters

ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27268185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27268185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27268185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size17.2 MiB
debt_consolidation
1277877 
credit_card
516971 
home_improvement
150457 
other
139440 
major_purchase
 
50445
Other values (9)
 
125478

Length

Max length18
Median length18
Mean length14.792476
Min length3

Characters and Unicode

Total characters33440877
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowsmall_business
3rd rowhome_improvement
4th rowdebt_consolidation
5th rowmajor_purchase

Common Values

ValueCountFrequency (%)
debt_consolidation 1277877
56.5%
credit_card 516971
22.9%
home_improvement 150457
 
6.7%
other 139440
 
6.2%
major_purchase 50445
 
2.2%
medical 27488
 
1.2%
small_business 24689
 
1.1%
car 24013
 
1.1%
vacation 15525
 
0.7%
moving 15403
 
0.7%
Other values (4) 18360
 
0.8%

Length

2025-07-17T10:55:18.866951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 1277877
56.5%
credit_card 516971
22.9%
home_improvement 150457
 
6.7%
other 139440
 
6.2%
major_purchase 50445
 
2.2%
medical 27488
 
1.2%
small_business 24689
 
1.1%
car 24013
 
1.1%
vacation 15525
 
0.7%
moving 15403
 
0.7%
Other values (4) 18360
 
0.8%

Most occurring characters

ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33440877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33440877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33440877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

dti
Real number (ℝ)

Skewed 

Distinct10845
Distinct (%)0.5%
Missing1744
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean18.824196
Minimum-1
Maximum999
Zeros1732
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size17.2 MiB
2025-07-17T10:55:19.002540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4.94
Q111.89
median17.84
Q324.49
95-th percentile33.88
Maximum999
Range1000
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation14.183329
Coefficient of variation (CV)0.75346263
Kurtosis1755.2613
Mean18.824196
Median Absolute Deviation (MAD)6.27
Skewness29.201854
Sum42523050
Variance201.16681
MonotonicityNot monotonic
2025-07-17T10:55:19.050256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1732
 
0.1%
18 1584
 
0.1%
14.4 1577
 
0.1%
16.8 1576
 
0.1%
19.2 1566
 
0.1%
15.6 1506
 
0.1%
13.2 1496
 
0.1%
12 1486
 
0.1%
20.4 1424
 
0.1%
21.6 1391
 
0.1%
Other values (10835) 2243619
99.2%
(Missing) 1744
 
0.1%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 1732
0.1%
0.01 22
 
< 0.1%
0.02 35
 
< 0.1%
0.03 19
 
< 0.1%
0.04 16
 
< 0.1%
0.05 20
 
< 0.1%
0.06 32
 
< 0.1%
0.07 27
 
< 0.1%
0.08 30
 
< 0.1%
ValueCountFrequency (%)
999 135
< 0.1%
995.6 1
 
< 0.1%
995.17 1
 
< 0.1%
994.4 1
 
< 0.1%
991.57 1
 
< 0.1%
973.17 1
 
< 0.1%
962.83 1
 
< 0.1%
942.17 1
 
< 0.1%
941.46 1
 
< 0.1%
917.87 1
 
< 0.1%

fico_range_low
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean698.5882
Minimum610
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:19.096155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile660
Q1675
median690
Q3715
95-th percentile765
Maximum845
Range235
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.010376
Coefficient of variation (CV)0.047252983
Kurtosis1.3151684
Mean698.5882
Median Absolute Deviation (MAD)20
Skewness1.1928772
Sum1.579276 × 109
Variance1089.685
MonotonicityNot monotonic
2025-07-17T10:55:19.143604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
660 186580
 
8.3%
670 182119
 
8.1%
665 180759
 
8.0%
680 167199
 
7.4%
675 164016
 
7.3%
685 148009
 
6.5%
690 144690
 
6.4%
695 130941
 
5.8%
700 124184
 
5.5%
705 113074
 
5.0%
Other values (38) 719097
31.8%
ValueCountFrequency (%)
610 2
 
< 0.1%
615 1
 
< 0.1%
620 1
 
< 0.1%
625 2
 
< 0.1%
630 6
 
< 0.1%
635 5
 
< 0.1%
640 102
< 0.1%
645 112
< 0.1%
650 131
< 0.1%
655 127
< 0.1%
ValueCountFrequency (%)
845 441
 
< 0.1%
840 572
 
< 0.1%
835 859
 
< 0.1%
830 1439
 
0.1%
825 2189
 
0.1%
820 2825
 
0.1%
815 3845
0.2%
810 4720
0.2%
805 6442
0.3%
800 7716
0.3%

fico_range_high
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean702.5884
Minimum614
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:19.190802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum614
5-th percentile664
Q1679
median694
Q3719
95-th percentile769
Maximum850
Range236
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.011245
Coefficient of variation (CV)0.046985183
Kurtosis1.3167697
Mean702.5884
Median Absolute Deviation (MAD)20
Skewness1.1931165
Sum1.5883191 × 109
Variance1089.7423
MonotonicityNot monotonic
2025-07-17T10:55:19.236415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
664 186580
 
8.3%
674 182119
 
8.1%
669 180759
 
8.0%
684 167199
 
7.4%
679 164016
 
7.3%
689 148009
 
6.5%
694 144690
 
6.4%
699 130941
 
5.8%
704 124184
 
5.5%
709 113074
 
5.0%
Other values (38) 719097
31.8%
ValueCountFrequency (%)
614 2
 
< 0.1%
619 1
 
< 0.1%
624 1
 
< 0.1%
629 2
 
< 0.1%
634 6
 
< 0.1%
639 5
 
< 0.1%
644 102
< 0.1%
649 112
< 0.1%
654 131
< 0.1%
659 127
< 0.1%
ValueCountFrequency (%)
850 441
 
< 0.1%
844 572
 
< 0.1%
839 859
 
< 0.1%
834 1439
 
0.1%
829 2189
 
0.1%
824 2825
 
0.1%
819 3845
0.2%
814 4720
0.2%
809 6442
0.3%
804 7716
0.3%

open_acc
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.612402
Minimum0
Maximum101
Zeros56
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:19.283789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q314
95-th percentile22
Maximum101
Range101
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.6408613
Coefficient of variation (CV)0.4857618
Kurtosis3.4463768
Mean11.612402
Median Absolute Deviation (MAD)3
Skewness1.315545
Sum26251449
Variance31.819317
MonotonicityNot monotonic
2025-07-17T10:55:19.329635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 195762
 
8.7%
10 189737
 
8.4%
8 188717
 
8.3%
11 175101
 
7.7%
7 172834
 
7.6%
12 157331
 
7.0%
6 145444
 
6.4%
13 137502
 
6.1%
14 118314
 
5.2%
5 108565
 
4.8%
Other values (81) 671332
29.7%
ValueCountFrequency (%)
0 56
 
< 0.1%
1 1644
 
0.1%
2 10860
 
0.5%
3 32428
 
1.4%
4 67827
 
3.0%
5 108565
4.8%
6 145444
6.4%
7 172834
7.6%
8 188717
8.3%
9 195762
8.7%
ValueCountFrequency (%)
101 1
 
< 0.1%
97 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
88 1
 
< 0.1%
86 3
< 0.1%
84 1
 
< 0.1%
82 2
< 0.1%

revol_bal
Real number (ℝ)

Distinct102251
Distinct (%)4.5%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16658.458
Minimum0
Maximum2904836
Zeros12562
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:19.374634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1572
Q15950
median11324
Q320246
95-th percentile45164
Maximum2904836
Range2904836
Interquartile range (IQR)14296

Descriptive statistics

Standard deviation22948.305
Coefficient of variation (CV)1.3775768
Kurtosis643.19806
Mean16658.458
Median Absolute Deviation (MAD)6381
Skewness13.231988
Sum3.7659243 × 1010
Variance5.266247 × 108
MonotonicityNot monotonic
2025-07-17T10:55:19.419816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12562
 
0.6%
8 216
 
< 0.1%
10 170
 
< 0.1%
2 169
 
< 0.1%
5 160
 
< 0.1%
5235 160
 
< 0.1%
6312 158
 
< 0.1%
5849 158
 
< 0.1%
5265 156
 
< 0.1%
6118 156
 
< 0.1%
Other values (102241) 2246603
99.4%
ValueCountFrequency (%)
0 12562
0.6%
1 123
 
< 0.1%
2 169
 
< 0.1%
3 151
 
< 0.1%
4 153
 
< 0.1%
5 160
 
< 0.1%
6 149
 
< 0.1%
7 128
 
< 0.1%
8 216
 
< 0.1%
9 141
 
< 0.1%
ValueCountFrequency (%)
2904836 1
< 0.1%
2568995 1
< 0.1%
2560703 1
< 0.1%
2559552 1
< 0.1%
2358150 1
< 0.1%
1830688 1
< 0.1%
1803041 1
< 0.1%
1746716 1
< 0.1%
1743266 1
< 0.1%
1698749 1
< 0.1%

revol_util
Real number (ℝ)

Distinct1430
Distinct (%)0.1%
Missing1835
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean50.337696
Minimum0
Maximum892.3
Zeros13069
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:19.465918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.4
Q131.5
median50.3
Q369.4
95-th percentile91
Maximum892.3
Range892.3
Interquartile range (IQR)37.9

Descriptive statistics

Standard deviation24.713073
Coefficient of variation (CV)0.49094566
Kurtosis-0.22267175
Mean50.337696
Median Absolute Deviation (MAD)18.9
Skewness0.012555943
Sum1.1370611 × 108
Variance610.73599
MonotonicityNot monotonic
2025-07-17T10:55:19.512058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13069
 
0.6%
57 4324
 
0.2%
48 4283
 
0.2%
59 4272
 
0.2%
61 4223
 
0.2%
54 4190
 
0.2%
58 4188
 
0.2%
53 4185
 
0.2%
55 4181
 
0.2%
51 4175
 
0.2%
Other values (1420) 2207776
97.7%
ValueCountFrequency (%)
0 13069
0.6%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.1 1684
 
0.1%
0.12 1
 
< 0.1%
0.16 1
 
< 0.1%
0.2 1360
 
0.1%
0.3 1272
 
0.1%
ValueCountFrequency (%)
892.3 1
< 0.1%
366.6 1
< 0.1%
193 1
< 0.1%
191 1
< 0.1%
184.6 1
< 0.1%
183.8 1
< 0.1%
182.8 1
< 0.1%
180.3 1
< 0.1%
177.7 1
< 0.1%
175 1
< 0.1%

total_acc
Real number (ℝ)

High correlation 

Distinct152
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.162552
Minimum1
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-17T10:55:19.555822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q115
median22
Q331
95-th percentile46
Maximum176
Range175
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.987528
Coefficient of variation (CV)0.49612012
Kurtosis1.8488152
Mean24.162552
Median Absolute Deviation (MAD)8
Skewness1.0074555
Sum54622808
Variance143.70084
MonotonicityNot monotonic
2025-07-17T10:55:19.603894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 82570
 
3.7%
19 82012
 
3.6%
18 81931
 
3.6%
17 81378
 
3.6%
21 81170
 
3.6%
16 79655
 
3.5%
22 79438
 
3.5%
23 77691
 
3.4%
15 77146
 
3.4%
24 75330
 
3.3%
Other values (142) 1462318
64.7%
ValueCountFrequency (%)
1 21
 
< 0.1%
2 1333
 
0.1%
3 4244
 
0.2%
4 10456
 
0.5%
5 16398
 
0.7%
6 23858
1.1%
7 31471
1.4%
8 38722
1.7%
9 46406
2.1%
10 53576
2.4%
ValueCountFrequency (%)
176 1
 
< 0.1%
173 1
 
< 0.1%
169 1
 
< 0.1%
165 1
 
< 0.1%
162 1
 
< 0.1%
160 2
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
153 1
 
< 0.1%
151 3
< 0.1%

loan_status
Categorical

Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size17.2 MiB
Fully Paid
1076751 
Current
878317 
Charged Off
268559 
Late (31-120 days)
 
21467
In Grace Period
 
8436
Other values (4)
 
7138

Length

Max length51
Median length50
Mean length9.1102488
Min length7

Characters and Unicode

Total characters20595248
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowCurrent
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 1076751
47.6%
Current 878317
38.9%
Charged Off 268559
 
11.9%
Late (31-120 days) 21467
 
0.9%
In Grace Period 8436
 
0.4%
Late (16-30 days) 4349
 
0.2%
Does not meet the credit policy. Status:Fully Paid 1988
 
0.1%
Does not meet the credit policy. Status:Charged Off 761
 
< 0.1%
Default 40
 
< 0.1%
(Missing) 33
 
< 0.1%

Length

2025-07-17T10:55:19.647482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T10:55:19.681197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
paid 1078739
29.2%
fully 1076751
29.2%
current 878317
23.8%
off 269320
 
7.3%
charged 268559
 
7.3%
late 25816
 
0.7%
days 25816
 
0.7%
31-120 21467
 
0.6%
grace 8436
 
0.2%
period 8436
 
0.2%
Other values (11) 32068
 
0.9%

Most occurring characters

ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20595248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20595248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20595248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%

Interactions

2025-07-17T10:55:07.555751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:49.445538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:51.326248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:53.198923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:55.101121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:56.744047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:58.461303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:00.295114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:02.228582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:04.061731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:05.743986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:07.760832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:49.626023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-17T10:54:56.902687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:58.632933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-17T10:54:54.949596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:56.585698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:54:58.289046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:00.126111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:02.060193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:03.912361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:05.579937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T10:55:07.383015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-17T10:55:19.733464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
annual_incdtiemp_lengthfico_range_highfico_range_lowgradehome_ownershipinstallmentint_rateloan_amntloan_statusopen_accpurposerevol_balrevol_utilsub_gradetermtotal_accverification_status
annual_inc1.000-0.2010.0000.0870.0870.0000.0000.446-0.1330.4670.0000.2670.0000.4140.0800.0000.0010.3290.000
dti-0.2011.0000.008-0.019-0.0190.0020.0050.0630.1860.0560.0100.3260.0010.2690.1760.0030.0110.2580.013
emp_length0.0000.0081.0000.0130.0130.0070.0910.0260.0070.0310.0170.0180.0230.0030.0080.0070.0570.0400.038
fico_range_high0.087-0.0190.0131.0001.0000.2000.0530.069-0.4350.1210.1570.0370.0450.020-0.4210.1800.0420.0220.124
fico_range_low0.087-0.0190.0131.0001.0000.2000.0530.069-0.4350.1210.1570.0370.0450.020-0.4210.1800.0420.0220.124
grade0.0000.0020.0070.2000.2001.0000.0360.0770.6860.0730.0980.0140.0760.0030.0611.0000.3850.0230.176
home_ownership0.0000.0050.0910.0530.0530.0361.0000.0670.0380.0810.0370.0590.0870.0090.0080.0410.1090.0990.032
installment0.4460.0630.0260.0690.0690.0770.0671.0000.1120.9640.0260.1920.0930.4450.1370.0710.3220.1990.153
int_rate-0.1330.1860.007-0.435-0.4350.6860.0380.1121.0000.0940.079-0.0190.062-0.0150.2870.7250.370-0.0540.182
loan_amnt0.4670.0560.0310.1210.1210.0730.0810.9640.0941.0000.0490.2010.0990.4580.1140.0630.4410.2190.156
loan_status0.0000.0100.0170.1570.1570.0980.0370.0260.0790.0491.0000.0120.0420.0060.0150.0880.1810.0300.088
open_acc0.2670.3260.0180.0370.0370.0140.0590.192-0.0190.2010.0121.0000.0260.395-0.1320.0140.0700.7150.017
purpose0.0000.0010.0230.0450.0450.0760.0870.0930.0620.0990.0420.0261.0000.0050.0160.0540.1020.0270.065
revol_bal0.4140.2690.0030.0200.0200.0030.0090.445-0.0150.4580.0060.3950.0051.0000.4450.0030.0010.3250.010
revol_util0.0800.1760.008-0.421-0.4210.0610.0080.1370.2870.1140.015-0.1320.0160.4451.0000.0620.025-0.0860.052
sub_grade0.0000.0030.0070.1800.1801.0000.0410.0710.7250.0630.0880.0140.0540.0030.0621.0000.3950.0220.190
term0.0010.0110.0570.0420.0420.3850.1090.3220.3700.4410.1810.0700.1020.0010.0250.3951.0000.0940.098
total_acc0.3290.2580.0400.0220.0220.0230.0990.199-0.0540.2190.0300.7150.0270.325-0.0860.0220.0941.0000.031
verification_status0.0000.0130.0380.1240.1240.1760.0320.1530.1820.1560.0880.0170.0650.0100.0520.1900.0980.0311.000

Missing values

2025-07-17T10:55:09.787515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-17T10:55:11.499827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-17T10:55:15.918648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loan_amnttermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statuspurposedtifico_range_lowfico_range_highopen_accrevol_balrevol_utiltotal_accloan_status
03600.036 months13.99123.03CC4leadman10+ yearsMORTGAGE55000.0Not Verifieddebt_consolidation5.91675.0679.07.02765.029.713.0Fully Paid
124700.036 months11.99820.28CC1Engineer10+ yearsMORTGAGE65000.0Not Verifiedsmall_business16.06715.0719.022.021470.019.238.0Fully Paid
220000.060 months10.78432.66BB4truck driver10+ yearsMORTGAGE63000.0Not Verifiedhome_improvement10.78695.0699.06.07869.056.218.0Fully Paid
335000.060 months14.85829.90CC5Information Systems Officer10+ yearsMORTGAGE110000.0Source Verifieddebt_consolidation17.06785.0789.013.07802.011.617.0Current
410400.060 months22.45289.91FF1Contract Specialist3 yearsMORTGAGE104433.0Source Verifiedmajor_purchase25.37695.0699.012.021929.064.535.0Fully Paid
511950.036 months13.44405.18CC3Veterinary Tecnician4 yearsRENT34000.0Source Verifieddebt_consolidation10.20690.0694.05.08822.068.46.0Fully Paid
620000.036 months9.17637.58BB2Vice President of Recruiting Operations10+ yearsMORTGAGE180000.0Not Verifieddebt_consolidation14.67680.0684.012.087329.084.527.0Fully Paid
720000.036 months8.49631.26BB1road driver10+ yearsMORTGAGE85000.0Not Verifiedmajor_purchase17.61705.0709.08.0826.05.715.0Fully Paid
810000.036 months6.49306.45AA2SERVICE MANAGER6 yearsRENT85000.0Not Verifiedcredit_card13.07685.0689.014.010464.034.523.0Fully Paid
98000.036 months11.48263.74BB5Vendor liaison10+ yearsMORTGAGE42000.0Not Verifiedcredit_card34.80700.0704.08.07034.039.118.0Fully Paid
loan_amnttermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statuspurposedtifico_range_lowfico_range_highopen_accrevol_balrevol_utiltotal_accloan_status
226069132000.060 months14.49752.74CC4Sales Manager3 yearsMORTGAGE157000.0Source Verifiedhome_improvement10.34735.0739.014.0111598.027.418.0Charged Off
226069216000.060 months12.79362.34CC1Manager10+ yearsRENT150000.0Not Verifiedmedical12.25665.0669.012.07700.055.028.0Fully Paid
226069324000.060 months10.49515.74BB3Current Operations Officer4 yearsOWN125000.0Not Verifiedcredit_card10.98725.0729.015.022448.022.422.0Current
226069424000.060 months12.79543.50CC1Unit Operator7 yearsMORTGAGE95000.0Source Verifiedhome_improvement19.61665.0669.05.049431.084.454.0Current
226069524000.060 months10.49515.74BB3Database Administrator10+ yearsMORTGAGE108000.0Not Verifieddebt_consolidation34.94695.0699.024.021665.039.058.0Current
226069640000.060 months10.49859.56BB3Vice President9 yearsMORTGAGE227000.0Verifieddebt_consolidation12.75705.0709.05.08633.064.937.0Current
226069724000.060 months14.49564.56CC4Program Manager6 yearsRENT110000.0Not Verifieddebt_consolidation18.30660.0664.010.017641.068.131.0Charged Off
226069814000.060 months14.49329.33CC4Customer Service Technician10+ yearsMORTGAGE95000.0Verifieddebt_consolidation23.36660.0664.08.07662.054.022.0Current
2260699NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2260700NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

loan_amnttermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statuspurposedtifico_range_lowfico_range_highopen_accrevol_balrevol_utiltotal_accloan_status# duplicates
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN33